• Industrial Engineering and Management Systems
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• 제13권4호
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• pp.357-368
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• 2014

Since its birth in 1993, fuzzy time series have seen different classes of models designed and applied, such as fuzzy logic relation and rule-based models. These models have both advantages and disadvantages. The major drawbacks with these two classes of models are the difficulties encountered in identification and analysis of the model. Therefore, there is a strong need to explore new alternatives and this is the objective of this paper. By transforming a fuzzy number to a real number via integrating the inverse of the membership function, new autoregressive models can be developed to fit the observation values of a fuzzy time series. With the new models, the issues of model identification and parameter estimation can be addressed; and trends, seasonalities and multivariate fuzzy time series could also be modeled with ease. In addition, asymptotic behaviors of fuzzy time series can be inspected by means of characteristic equations.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.335-348
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• 2009

Fuzzy regression is used as a complement or an alternative to represent the relation between variables among the forecasting models especially when the data is insufficient to evaluate the relation. Such phenomenon often occurs in seasonal time series data which require large amount of data to describe the underlying pattern. Semiparametric model is useful tool in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. In this paper we propose fuzzy semiparametric support vector regression so that it can provide good performance on forecasting of the seasonal time series by incorporating into fuzzy support vector regression the basis functions which indicate the seasonal variation of time series. In order to indicate the performance of this method, we present two examples of predicting the seasonal time series. Experimental results show that the proposed method is very attractive for the seasonal time series in fuzzy environments.

• 대한원격탐사학회지
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• 제36권4호
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• pp.527-534
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• 2020

This study aims to monitor the displacement of the bridges using Stanford Method for Persistent Scatterers (StaMPS) time-series Persistent Scatterer Interferometric Synthetic Aperture Radar analysis. For case study bridges: Kimdaejung bridge and Deokyang bridge, we acquired 60 and 33 Cosmo-Skymed Synthetic Aperture Radar (SAR) data over the Mokpo region and Yeosu region, respectively from 2013 to 2019. With single-look interferograms, we estimated the long-term time-series displacements over the bridges. The time-series displacements were estimated as -8.8 mm/year and -1.34 mm/year at the mid-span over the selected bridges: Kimdaejung and Deokyang bridge, respectively. This time-series displacement provides reliable and high spatial resolution information to monitor the structural behavior of the bridge for preventing structural behaviors.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.1450-1454
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• 2006

Empirical mode decomposition (EMD) is applied to analyze time series characterized with nonlinearity and nonstationarity. This decomposition could be utilized to construct finite and small number intrinsic mode functions (IMF) that describe complicated time series, while admitting the Hilbert transformation properties. EMD has the capability of being adaptive, capture local characteristics, and applicable to nonlinear and nonstationary processes. Unlike discrete wavelet transform (DWT), IMF eliminates spurious harmonics and retains meaningful instantaneous frequencies. Examples based on data representing natural phenomena are given to demonstrate highlight the power of this method in contrast and comparison of other ones. A presentation of the energy-frequency-time distribution of these signals found to be more informative and intuitive when based on Hilbert transformation.

• 한국환경과학회지
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• 제9권1호
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• pp.19-25
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• 2000

As the tideland reclamation is done on a large scale these days, construction work is active in the coastal areas. Facilities in the coastal areas must be built with the tide characteristics taken into consideration. Thus the tide characteristics affect the overall reclamation plan. The analysis of the tide data boils down to a harmonic analysis of the hourly changes of long-term tide data and extraction of unharmonic coefficients from the results. Since considerable amount of tide data of the West Coast are available, the existing data can be collected and can be used to obtain the temporal changes of the tide by being fitted into the tide prediction model. The goal of this thesis lies in assessing whether the mean sea level used in the field agrees with the analysis results from the long-term observation data obtained with their homogeneity guaranteed. To achieve this goal, the research was conducted as follows. First the present conditions of the observation stations, the land level standard, and the sea level standard were analyzed to set up a time series model formula for representing them. To secure the homogeneity of the time series, each component was separated. Lastly the mean sea level used in the field was assessed based on the results obtained form the analysis of the time series.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.792-796
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• 2005

The methods of times series analysis have been recognized as important tools for assisting in solving problems related to the management of water resources. Especially, After more than 40 years the so-called Hurst effect remains an open problem in stochastic hydrology. Until now, its existence has been explained fly R/S analysis that roots in early work of the British hydrologist H.E. Hurst(1951). Today, the Hurst analysis is mostly used for the hydrological studies for memory and characteristics of time series and many methodologies have been developed for the analysis. So, there are many different techniques for the estimation of the Hurst exponent(H). However, the techniques can produce different characteristics for the persistence of a time series each other. We found that DFA is the most appropriate technique for the Hurst exponent estimation for both the shot term memory and long term memory. We analyze the SOI(Southern Oscillations Index) and 6 tree-ring series for USA sites by means of DFA and the BDS statistic is used for nonlinearity test of the series. From the results, we found that SOI series is nonlinear time series which has a long term memory of H=0.92. Contrary to earlier work of Rao(1999), all the tree- ring series are not random from our analysis. A certain tree ring series show a long term memory of H=0.97 and nonlinear property. Therefore, we can say that the SOI and tree-ring series may show long memory and nonlinearity.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2009년도 추계학술발표대회 논문집
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• pp.26-31
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• 2009

This study was analysed influence of urban higher temperature in Busan about time series analysis of AWS data. The results are as follows. (1) The temperature of Busan show min $13.2^{\circ}C$ ~max $15.8^{\circ}C$ by 50 years, it is on the rise. (2) The seasonal adjustment series, summer appeared min $17.5^{\circ}C$ ~max $28.9^{\circ}C$ with primitive series similarly. The winter was min $-11.4^{\circ}C$ ~max $17.9^{\circ}C$, the minimum temperature was more lowly than primitive series and maximum temperature was more higher than primitive series. The results, seasonal adjustment series is guessed with influence difference urban structural element beside seasonal factor. (3) Regional analytical result, January appeared with range of min 28% ~max 196% of the seasonal factor and August appeared min 90% ~ max 106%. One of the case which is of 100% or more of the seasonal factor January 12nd~17th, August appears at the 15~17th.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2011년도 제37회 추계학술대회논문집
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• pp.241-245
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• 2011

엔진 시험과정에서 데이터는 시계열 형태로 수집된다. 보통 그러한 시계열들의 섭동보다는 시간평균에 더 관심을 가진다. 본 논문에서는 공기 흡입식 엔진의 시험에서 측정된 압력과 유량 데이터의 섭동에 시계열의 복잡성의 척도로 제안된 개념인 multiscale sample entropy라는 분석법을 적용해본다. 분석 결과, 서로 다른 물리량은 각각의 시간척도에서 다른 복잡성을 가진다는 것을 보였고, 이를 잘 이용하면 엔진 시험의 성패 여부를 즉각적으로 알려주는 도구를 만들 수 있을 것이다.

• 대한토목학회논문집
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• 제26권2B호
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• pp.131-137
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• 2006

본 연구에서는 기존 매개변수적 수문시계열 예측모형을 보완하고자 Singular Spectrum Analysis(SSA)와 Linear Recurrent Formula를 결합한 모형을 제안하였다. SSA는 주로 시계열에 내재해 있는 구성성분을 추출하기 위한 목적으로 많이 이용되고 있다. 이러한 관점에서 본 연구에서는 엘니뇨 및 라니냐 등의 기상현상과 수문사상의 상관성 분석에 주로 적용되고 있는 SSA와 시계열 예측을 위해서 Linear Recurrence Formula를 결합한 예측 모형을 월단위의 수위와 유입량 시계열 자료를 대상으로 적용성 및 타당성을 검토해 보았다. 모형을 통해 수문시계열을 모의한 결과 전체적인 통계적인 특성 및 시각적인 검토에서 실측자료와 매우 유사한 모의가 가능하였으며 실측 자료를 바탕으로 Blind Forecasting을 실시한 결과 2가지 예에서 모두 1년 정도의 예측구간에서 합리적인 결과를 제시하여 주었다. 따라서 단기예측을 수문모형으로서 적용이 가능할 것으로 사료된다.

• 한국공작기계학회논문집
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• 제14권5호
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• pp.7-12
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• 2005

This paper presents several time series methods to analyze the chatter mechanics by using the power spectrum of these algorithms considering the cutting dynamics. In this study, several time series models such as AR(burg, forwardbackward, geometric lattice, instrument variable, least square, Yule Walker), ARX(1s, iv4), ARMAX, ARMA, Box Jenkins, Output Error were modeled and compared with one another. Finally, it was proven that time series modelings are also a desirable and reliable algorithm than the other conventional methods(FFT) for the calculation of the chatter mode in turning operation. Also, the spectrum of times series methods is a little bit more powerful than the FFT fer the detection of a high noisy and weak chatter mode. The radial cutting force Fy has been used for spectrum and chatter stability lobe analysis in this study.